$47M+ · AI + Financial Data

What they're building

Daloopa is the plumbing that makes AI actually useful in finance. The problem it solves is specific and consequential: when an AI agent produces a financial analysis, the answer is only as reliable as the data it retrieved. Most AI-accessible financial data is web-scraped, inconsistently labelled, and not traceable to an original filing. When the data is wrong, the analysis is wrong, and in investment decisions, that costs money. Daloopa's platform covers 5,500+ public companies globally. Every data point is extracted directly from the original source document (SEC filing, investor presentation, earnings release) and hyperlinked back to it. Fiscal calendars are normalised. Metric definitions are standardised across companies. Historical data goes back up to 14 years. The platform delivers 10 times more data points per company than competing providers. In a benchmark study, AI agent accuracy improved by up to 71 percentage points when grounded in Daloopa's auditable dataset versus web-based retrieval. Delivery is format-agnostic: Excel add-in for traditional analysts, API for programmatic access, cloud-native delivery via Snowflake, Databricks, and AWS S3 for enterprise data teams, and MCP connectors that plug directly into Claude, ChatGPT, Perplexity, and Rogo for agentic workflows. The Partner API launched recently allows third-party developers to build on the data layer directly. The product is not theoretical. 160+ financial institutions are paying customers, Anthropic and OpenAI use it, and the company has doubled revenue year-on-year. Fast Company ranked Daloopa #10 in the Emerging Enterprise category in its 2026 Most Innovative Companies list.

Why this matters

Investment research is one of the largest and most data-intensive white-collar workflows in the world. For decades, it has run on a manual process: analysts pulling numbers from filings, cleaning and reconciling them, building and updating Excel models, before getting to any actual analysis. AI has been able to do the analysis part for two years. The bottleneck is the data input: if the figures going in are wrong, the analysis coming out is wrong. The AI in financial services adoption curve makes this urgent. The Cambridge Centre for Alternative Finance's 2026 Global AI in Financial Services Report found 81% of surveyed firms are now adopting AI, with 40% at advanced adoption levels. Advanced adoption means production workflows, not pilots. Production workflows require data that is accurate, auditable, and traceable. That is Daloopa's entire product proposition. Brighton Park's special advisor for this round was Phil Hadley, former CEO and Chairman of FactSet, one of the largest financial data companies in the world (valued at approximately $17 billion). FactSet and Bloomberg Terminal together have dominated financial data infrastructure for decades. Hadley's involvement signals that Brighton Park understands financial data at the incumbent level and believes Daloopa is building something that matters in the next era. When the former CEO of your most relevant incumbent competitor advises the lead investor, the due diligence is unusually rigorous and the conviction is correspondingly high. Squarepoint Capital's participation is the user validation signal. Squarepoint is a quantitative hedge fund that manages billions in capital through algorithmic strategies. They do not make venture bets for financial returns at their scale. They invested because Daloopa's data is already in their research infrastructure and they want to ensure it remains well-resourced and competitive.